Instructions to use QuantFactory/reader-lm-0.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/reader-lm-0.5b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/reader-lm-0.5b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/reader-lm-0.5b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/reader-lm-0.5b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/reader-lm-0.5b-GGUF",
	filename="reader-lm-0.5b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/reader-lm-0.5b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/reader-lm-0.5b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/reader-lm-0.5b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/reader-lm-0.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M

SGLang

How to use QuantFactory/reader-lm-0.5b-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/reader-lm-0.5b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/reader-lm-0.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/reader-lm-0.5b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/reader-lm-0.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/reader-lm-0.5b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/reader-lm-0.5b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/reader-lm-0.5b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/reader-lm-0.5b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/reader-lm-0.5b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/reader-lm-0.5b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/reader-lm-0.5b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/reader-lm-0.5b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.reader-lm-0.5b-GGUF-Q4_K_M

List all available models

lemonade list

aashish1904 commited on Sep 12, 2024

Commit

4575b79

verified ·

1 Parent(s): f3290f3

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README.md +81 -0

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	@@ -0,0 +1,81 @@

+---
+pipeline_tag: text-generation
+language:
+- multilingual
+inference: false
+license: cc-by-nc-4.0
+library_name: transformers
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/reader-lm-0.5b-GGUF
+This is quantized version of [jinaai/reader-lm-0.5b](https://huggingface.co/jinaai/reader-lm-0.5b) created using llama.cpp
+# Original Model Card
+<br><br>
+<p align="center">
+<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
+</p>
+<p align="center">
+<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
+</p>
+# Intro
+Jina Reader-LM is a series of models that convert HTML content to Markdown content, which is useful for content conversion tasks. The model is trained on a curated collection of HTML content and its corresponding Markdown content.
+# Models
+| Name            |  Context Length   | Download                                                                                                                                          |
+|-----------------|-------------------|-----------------------------------------------------------------------|
+| reader-lm-0.5b  |  256K             | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-0.5b)       |
+| reader-lm-1.5b  |  256K             | [🤗 Hugging Face](https://huggingface.co/jinaai/reader-lm-1.5b)       |
+|                 |
+# Evaluation
+TBD
+# Quick Start
+To use this model, you need to install `transformers`:
+```bash
+pip install transformers<=4.43.4
+```
+Then, you can use the model as follows:
+```python
+# pip install transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "jinaai/reader-lm-0.5b"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+# example html content
+html_content = "<html><body><h1>Hello, world!</h1></body></html>"
+messages = [{"role": "user", "content": html_content}]
+input_text=tokenizer.apply_chat_template(messages, tokenize=False)
+print(input_text)
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08)
+print(tokenizer.decode(outputs[0]))
+```